Top 10 best practices for next-gen analytics

By Anne-Lindsay Beall, SAS Insights Editor

Research shows that companies using analytics for decision making are 6% more profitable than those that don’t.1 “Companies understand the value of analytics,” says TDWI’s Fern Halper. “They want to be predictive and proactive,” Halper says, “and are pushing the envelope in terms of analytics and the platforms to support analysis.”

In Halper’s TDWI Best Practices Report: Next-Generation Analytics and Platforms for Business Success, she discusses how the phrase “next-generation analytics” evokes images of machine learning, big data, Hadoop, and the Internet of Things, but often means simply pushing past reports and dashboards to “more advanced forms of analytics, such as predictive analytics.”

Often a good first step into the world of advanced analytics is predictive analytics. Vendors are making the tools easier to use, and with the right controls in place, this can be a good place to start.

“Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis,” says Halper. The market is on the cusp of moving forward. To move with it, start with Halper’s top ten best practices below and read the full TDWI Best Practices Report: Next-Generation Analytics and Platforms for Business Success.

1. Realize there is no silver bullet, but don’t do nothing.

Building an analytics culture using next-generation analytics and putting the ecosystem together takes time. It’s important not to try to boil the ocean. However, it’s also important not to ignore the work and simply hope success will magically happen. Companies that are measuring value with analytics are taking risks, experimenting, and finding success. They’re evangelizing and communicating. It may take time, but they’re certainly getting there.

2. Consider new infrastructure technology.

Companies succeeding with next-generation analytics are putting together an ecosystem that consists of multiple technology types. Yes, this can include the data warehouse (don’t expect the new stuff to replace the old), but it should also include the right tools for the jobs, including in-memory computing for highly iterative analysis or the cloud to deal with vast amounts of data that might be generated in the public cloud and on premises.

3. Consider more advanced analytics.

Companies measuring value are using more advanced analytics. Although this requires skills and training, the upside is clear. Often a good first step into the world of advanced analytics is predictive analytics. Vendors are making the tools easier to use, and with the right controls in place, this can be a good place to start.

4. Start with a proof of concept.

Companies succeeding with predictive analytics often start with a metric they’re already measuring, so they can demonstrate that they can predict that metric -- they know it's valuable and will get attention.

5. Utilize disparate data.

Although structured data and demographic data are the mainstay of analysts and modelers, disparate data types can enrich a data set and provide lift to models. Think about incorporating data beyond the traditional types you might have in your data warehouse or on your servers. Good starting points include geospatial data and text.

6. Take training seriously.

The democratization of analytics is moving ahead. However, you need to think about the skills you’ll require for data management, as well as the skills to build your models and deal with your data. With statisticians and other quants in short supply, think about what skills you’ll need for the kinds of models you want to build. Part of the process is balancing the costs and benefits of the models you're considering. Allocate your resources wisely. Training will become an important part of your next-generation strategy.

7. Put controls in place.

Democratization means that business analysts will try to use more advanced technology. Make sure controls are in place before a model is put into production. This might include confirming the validity of a model.

8. Act on your data.

Analytics without action won’t yield measurable impact. Even if you aren’t ready to operationalize your analysis, it makes sense to start implementing a process to take action, even if it's manual action. You’ll be building a more analytically-driven culture for when you want to build more operational intelligence.

9. Build a center of excellence.

A CoE can be a great way to make sure that the infrastructure and analytics you implement are coherent. CoEs can help you disseminate information, provide training, and establish or maintain governance.

10. Remember to monitor your analysis.

Data can get stale. Models can get stale. It’s important to revisit any kind of analysis where action is taking place on a periodic basis to make sure that your data is still relevant and that your model still makes sense.